





"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   =  {
    "configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig", "RemBertOnnxConfig"]
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  ["RemBertTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  ["RemBertTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RemBertForCausalLM",
           "RemBertForMaskedLM",
           "RemBertForMultipleChoice",
           "RemBertForQuestionAnswering",
           "RemBertForSequenceClassification",
           "RemBertForTokenClassification",
           "RemBertLayer",
           "RemBertModel",
           "RemBertPreTrainedModel",
           "load_tf_weights_in_rembert",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFRemBertForCausalLM",
           "TFRemBertForMaskedLM",
           "TFRemBertForMultipleChoice",
           "TFRemBertForQuestionAnswering",
           "TFRemBertForSequenceClassification",
           "TFRemBertForTokenClassification",
           "TFRemBertLayer",
           "TFRemBertModel",
           "TFRemBertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_rembert import RemBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_rembert_fast import RemBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_rembert import (
                  REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RemBertForCausalLM,
                  RemBertForMaskedLM,
                  RemBertForMultipleChoice,
                  RemBertForQuestionAnswering,
                  RemBertForSequenceClassification,
                  RemBertForTokenClassification,
                  RemBertLayer,
                  RemBertModel,
                  RemBertPreTrainedModel,
                  load_tf_weights_in_rembert,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_rembert import (
                  TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRemBertForCausalLM,
                  TFRemBertForMaskedLM,
                  TFRemBertForMultipleChoice,
                  TFRemBertForQuestionAnswering,
                  TFRemBertForSequenceClassification,
                  TFRemBertForTokenClassification,
                  TFRemBertLayer,
                  TFRemBertModel,
                  TFRemBertPreTrainedModel,
              )


else:
       import sys

       SCREAMING_SNAKE_CASE__   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)













"""simple docstring"""





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


SCREAMING_SNAKE_CASE__   =  get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
       SCREAMING_SNAKE_CASE__   =  json.load(f)

@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self ,		lowercase					)						->	Tuple:
            return FSMTTokenizer.from_pretrained(lowercase					)





      def        _snake_case     (			self ,		lowercase					)						->	Dict:
            lowerCAmelCase    		=			FSMTForConditionalGeneration.from_pretrained(lowercase					).to(lowercase					)
            if torch_device == "cuda":
                  model.half()
            return model





      @parameterized.expand(
          [
              ["""en-ru""", 26.0],
              ["""ru-en""", 22.0],
              ["""en-de""", 22.0],
              ["""de-en""", 29.0],
          ]					)
      @slow
      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	Dict:
            # note: this test is not testing the best performance since it only evals a small batch
            # but it should be enough to detect a regression in the output quality
            lowerCAmelCase    		=			f'facebook/wmt19-{pair}'
            lowerCAmelCase    		=			self.get_tokenizer(lowercase					)
            lowerCAmelCase    		=			self.get_model(lowercase					)

            lowerCAmelCase    		=			bleu_data[pair]["""src"""]
            lowerCAmelCase    		=			bleu_data[pair]["""tgt"""]

            lowerCAmelCase    		=			tokenizer(lowercase ,		return_tensors="""pt""" ,		truncation=lowercase ,		padding="""longest"""					).to(lowercase					)
            lowerCAmelCase    		=			model.generate(
                input_ids=batch.input_ids ,		num_beams=8 ,		)
            lowerCAmelCase    		=			tokenizer.batch_decode(
                lowercase ,		skip_special_tokens=lowercase ,		clean_up_tokenization_spaces=lowercase					)
            lowerCAmelCase    		=			calculate_bleu(lowercase ,		lowercase					)
            print(lowercase					)
            self.assertGreaterEqual(scores["""bleu"""] ,		lowercase					)






